# Import packages
library(dplyr)
library(data.table)
library(ggplot2)# Setting environment
# remove(list=ls())
# setwd("C:\\Users\\sunil\\Downloads\\College\\DAV\\Project")
# make evironment not to change large number to exponential
options(scipen = 999)# Import dataset
nepal_dt <- read.csv("Source Dataset-API_NPL_DS2.csv", skip=4, header=TRUE, stringsAsFactors = FALSE)
meta_country <- read.csv("MetaData_Country.csv", header=TRUE, stringsAsFactors = FALSE)
meta_indictr <- read.csv("MetaData_Indicator.csv", header=TRUE, stringsAsFactors = FALSE)
nepal_dtmeta_countrymeta_indictrtemp_df = filter(nepal_dt, grepl("tax", tolower(IndicatorName), fixed = TRUE) | grepl("tax", tolower(IndicatorCode), fixed = TRUE))
nepal_df <- temp_df
nepal_dfdim(nepal_df)[1] 53 66temp_df = filter(nepal_dt, grepl("gdp", tolower(IndicatorName), fixed = TRUE) | grepl("gdp", tolower(IndicatorCode), fixed = TRUE))
nepal_df <- rbind(nepal_df, temp_df)
nepal_dfdim(nepal_df)[1] 233 66temp_df = filter(nepal_dt, grepl("employment", tolower(IndicatorName), fixed = TRUE) | grepl("employment", tolower(IndicatorCode), fixed = TRUE))
nepal_df <- rbind(nepal_df, temp_df)
nepal_df# Drop first and second column
nepal_df <- nepal_df[-c(1,2)]
nepal_df# unique(nepal_df$IndicatorName)
table(tolower(nepal_df$IndicatorName))
adequacy of unemployment benefits and almp (% of total welfare of beneficiary households)
1
agriculture, forestry, and fishing, value added (% of gdp)
1
benefit incidence of unemployment benefits and almp to poorest quintile (% of total u/almp benefits)
1
binding coverage, all products (%)
1
binding coverage, manufactured products (%)
1
binding coverage, primary products (%)
1
bound rate, simple mean, all products (%)
1
bound rate, simple mean, manufactured products (%)
1
bound rate, simple mean, primary products (%)
1
broad money (% of gdp)
1
central government debt, total (% of gdp)
1
child employment in agriculture (% of economically active children ages 7-14)
1
child employment in agriculture, female (% of female economically active children ages 7-14)
1
child employment in agriculture, male (% of male economically active children ages 7-14)
1
child employment in manufacturing (% of economically active children ages 7-14)
1
child employment in manufacturing, female (% of female economically active children ages 7-14)
1
child employment in manufacturing, male (% of male economically active children ages 7-14)
1
child employment in services (% of economically active children ages 7-14)
1
child employment in services, female (% of female economically active children ages 7-14)
1
child employment in services, male (% of male economically active children ages 7-14)
1
children in employment, female (% of female children ages 7-14)
1
children in employment, male (% of male children ages 7-14)
1
children in employment, self-employed (% of children in employment, ages 7-14)
1
children in employment, self-employed, female (% of female children in employment, ages 7-14)
1
children in employment, self-employed, male (% of male children in employment, ages 7-14)
1
children in employment, study and work (% of children in employment, ages 7-14)
1
children in employment, study and work, female (% of female children in employment, ages 7-14)
1
children in employment, study and work, male (% of male children in employment, ages 7-14)
1
children in employment, total (% of children ages 7-14)
1
children in employment, unpaid family workers (% of children in employment, ages 7-14)
1
children in employment, unpaid family workers, female (% of female children in employment, ages 7-14)
1
children in employment, unpaid family workers, male (% of male children in employment, ages 7-14)
1
children in employment, wage workers (% of children in employment, ages 7-14)
1
children in employment, wage workers, female (% of female children in employment, ages 7-14)
1
children in employment, wage workers, male (% of male children in employment, ages 7-14)
1
children in employment, work only (% of children in employment, ages 7-14)
1
children in employment, work only, female (% of female children in employment, ages 7-14)
1
children in employment, work only, male (% of male children in employment, ages 7-14)
1
claims on central government, etc. (% gdp)
1
claims on other sectors of the domestic economy (% of gdp)
1
co2 emissions (kg per 2015 us$ of gdp)
1
co2 emissions (kg per 2017 ppp $ of gdp)
1
co2 emissions (kg per ppp $ of gdp)
1
coal rents (% of gdp)
1
contributing family workers, female (% of female employment) (modeled ilo estimate)
1
contributing family workers, male (% of male employment) (modeled ilo estimate)
1
contributing family workers, total (% of total employment) (modeled ilo estimate)
1
coverage of unemployment benefits and almp (% of population)
1
coverage of unemployment benefits and almp in 2nd quintile (% of population)
1
coverage of unemployment benefits and almp in 3rd quintile (% of population)
1
coverage of unemployment benefits and almp in 4th quintile (% of population)
1
coverage of unemployment benefits and almp in poorest quintile (% of population)
1
coverage of unemployment benefits and almp in richest quintile (% of population)
1
current account balance (% of gdp)
1
current health expenditure (% of gdp)
1
customs and other import duties (% of tax revenue)
1
customs and other import duties (current lcu)
1
discrepancy in expenditure estimate of gdp (constant lcu)
1
discrepancy in expenditure estimate of gdp (current lcu)
1
domestic credit provided by financial sector (% of gdp)
1
domestic credit to private sector (% of gdp)
1
domestic credit to private sector by banks (% of gdp)
1
domestic general government health expenditure (% of gdp)
1
employers, female (% of female employment) (modeled ilo estimate)
1
employers, male (% of male employment) (modeled ilo estimate)
1
employers, total (% of total employment) (modeled ilo estimate)
1
employment in agriculture (% of total employment) (modeled ilo estimate)
1
employment in agriculture, female (% of female employment) (modeled ilo estimate)
1
employment in agriculture, male (% of male employment) (modeled ilo estimate)
1
employment in industry (% of total employment) (modeled ilo estimate)
1
employment in industry, female (% of female employment) (modeled ilo estimate)
1
employment in industry, male (% of male employment) (modeled ilo estimate)
1
employment in services (% of total employment) (modeled ilo estimate)
1
employment in services, female (% of female employment) (modeled ilo estimate)
1
employment in services, male (% of male employment) (modeled ilo estimate)
1
employment to population ratio, 15+, female (%) (modeled ilo estimate)
1
employment to population ratio, 15+, female (%) (national estimate)
1
employment to population ratio, 15+, male (%) (modeled ilo estimate)
1
employment to population ratio, 15+, male (%) (national estimate)
1
employment to population ratio, 15+, total (%) (modeled ilo estimate)
1
employment to population ratio, 15+, total (%) (national estimate)
1
employment to population ratio, ages 15-24, female (%) (modeled ilo estimate)
1
employment to population ratio, ages 15-24, female (%) (national estimate)
1
employment to population ratio, ages 15-24, male (%) (modeled ilo estimate)
1
employment to population ratio, ages 15-24, male (%) (national estimate)
1
employment to population ratio, ages 15-24, total (%) (modeled ilo estimate)
1
employment to population ratio, ages 15-24, total (%) (national estimate)
1
energy intensity level of primary energy (mj/$2017 ppp gdp)
1
energy use (kg of oil equivalent) per $1,000 gdp (constant 2017 ppp)
1
expense (% of gdp)
1
exports of goods and services (% of gdp)
1
external balance on goods and services (% of gdp)
1
female share of employment in senior and middle management (%)
1
final consumption expenditure (% of gdp)
1
firms expected to give gifts in meetings with tax officials (% of firms)
1
firms that do not report all sales for tax purposes (% of firms)
1
firms visited or required meetings with tax officials (% of firms)
1
foreign direct investment, net inflows (% of gdp)
1
foreign direct investment, net outflows (% of gdp)
1
forest rents (% of gdp)
1
gdp (constant 2015 us$)
1
gdp (constant lcu)
1
gdp (current lcu)
1
gdp (current us$)
1
gdp deflator (base year varies by country)
1
gdp deflator: linked series (base year varies by country)
1
gdp growth (annual %)
1
gdp per capita (constant 2015 us$)
1
gdp per capita (constant lcu)
1
gdp per capita (current lcu)
1
gdp per capita (current us$)
1
gdp per capita growth (annual %)
1
gdp per capita, ppp (constant 2017 international $)
1
gdp per capita, ppp (current international $)
1
gdp per person employed (constant 2017 ppp $)
1
gdp per unit of energy use (constant 2017 ppp $ per kg of oil equivalent)
1
gdp per unit of energy use (ppp $ per kg of oil equivalent)
1
gdp, ppp (constant 2017 international $)
1
gdp, ppp (current international $)
1
gdp: linked series (current lcu)
1
general government final consumption expenditure (% of gdp)
1
government expenditure on education, total (% of gdp)
1
government expenditure per student, primary (% of gdp per capita)
1
government expenditure per student, secondary (% of gdp per capita)
1
government expenditure per student, tertiary (% of gdp per capita)
1
gross capital formation (% of gdp)
1
gross domestic savings (% of gdp)
1
gross fixed capital formation (% of gdp)
1
gross fixed capital formation, private sector (% of gdp)
1
gross national expenditure (% of gdp)
1
gross savings (% of gdp)
1
gross value added at basic prices (gva) (constant 2015 us$)
1
gross value added at basic prices (gva) (constant lcu)
1
gross value added at basic prices (gva) (current lcu)
1
gross value added at basic prices (gva) (current us$)
1
households and npishs final consumption expenditure (% of gdp)
1
imports of goods and services (% of gdp)
1
industry (including construction), value added (% of gdp)
1
inflation, gdp deflator (annual %)
1
inflation, gdp deflator: linked series (annual %)
1
labor tax and contributions (% of commercial profits)
1
manufacturing, value added (% of gdp)
1
market capitalization of listed domestic companies (% of gdp)
1
merchandise trade (% of gdp)
1
military expenditure (% of gdp)
1
mineral rents (% of gdp)
1
monetary sector credit to private sector (% gdp)
1
natural gas rents (% of gdp)
1
net acquisition of financial assets (% of gdp)
1
net incurrence of liabilities, total (% of gdp)
1
net investment in nonfinancial assets (% of gdp)
1
net lending (+) / net borrowing (-) (% of gdp)
1
number of visits or required meetings with tax officials (average for affected firms)
1
oil rents (% of gdp)
1
other taxes (% of revenue)
1
other taxes (current lcu)
1
other taxes payable by businesses (% of commercial profits)
1
part time employment, female (% of total female employment)
1
part time employment, male (% of total male employment)
1
part time employment, total (% of total employment)
1
personal remittances, received (% of gdp)
1
ppp conversion factor, gdp (lcu per international $)
1
price level ratio of ppp conversion factor (gdp) to market exchange rate
1
profit tax (% of commercial profits)
1
research and development expenditure (% of gdp)
1
revenue, excluding grants (% of gdp)
1
self-employed, female (% of female employment) (modeled ilo estimate)
1
self-employed, male (% of male employment) (modeled ilo estimate)
1
self-employed, total (% of total employment) (modeled ilo estimate)
1
services, value added (% of gdp)
1
share of tariff lines with international peaks, all products (%)
1
share of tariff lines with international peaks, manufactured products (%)
1
share of tariff lines with international peaks, primary products (%)
1
share of tariff lines with specific rates, all products (%)
1
share of tariff lines with specific rates, manufactured products (%)
1
share of tariff lines with specific rates, primary products (%)
1
share of youth not in education, employment or training, female (% of female youth population)
1
share of youth not in education, employment or training, male (% of male youth population)
1
share of youth not in education, employment or training, total (% of youth population)
1
stocks traded, total value (% of gdp)
1
tariff rate, applied, simple mean, all products (%)
1
tariff rate, applied, simple mean, manufactured products (%)
1
tariff rate, applied, simple mean, primary products (%)
1
tariff rate, applied, weighted mean, all products (%)
1
tariff rate, applied, weighted mean, manufactured products (%)
1
tariff rate, applied, weighted mean, primary products (%)
1
tariff rate, most favored nation, simple mean, all products (%)
1
tariff rate, most favored nation, simple mean, manufactured products (%)
1
tariff rate, most favored nation, simple mean, primary products (%)
1
tariff rate, most favored nation, weighted mean, all products (%)
1
tariff rate, most favored nation, weighted mean, manufactured products (%)
1
tariff rate, most favored nation, weighted mean, primary products (%)
1
tax payments (number)
1
tax revenue (% of gdp)
2
tax revenue (current lcu)
1
taxes less subsidies on products (constant lcu)
1
taxes less subsidies on products (current lcu)
1
taxes less subsidies on products (current us$)
1
taxes on exports (% of tax revenue)
1
taxes on exports (current lcu)
1
taxes on goods and services (% of revenue)
1
taxes on goods and services (% value added of industry and services)
1
taxes on goods and services (current lcu)
1
taxes on income, profits and capital gains (% of revenue)
1
taxes on income, profits and capital gains (% of total taxes)
1
taxes on income, profits and capital gains (current lcu)
1
taxes on international trade (% of revenue)
1
taxes on international trade (current lcu)
1
time to prepare and pay taxes (hours)
1
total natural resources rents (% of gdp)
1
total tax and contribution rate (% of profit)
1
trade (% of gdp)
1
trade in services (% of gdp)
1
unemployment with advanced education (% of total labor force with advanced education)
1
unemployment with advanced education, female (% of female labor force with advanced education)
1
unemployment with advanced education, male (% of male labor force with advanced education)
1
unemployment with basic education (% of total labor force with basic education)
1
unemployment with basic education, female (% of female labor force with basic education)
1
unemployment with basic education, male (% of male labor force with basic education)
1
unemployment with intermediate education (% of total labor force with intermediate education)
1
unemployment with intermediate education, female (% of female labor force with intermediate education)
1
unemployment with intermediate education, male (% of male labor force with intermediate education)
1
unemployment, female (% of female labor force) (modeled ilo estimate)
1
unemployment, female (% of female labor force) (national estimate)
1
unemployment, male (% of male labor force) (modeled ilo estimate)
1
unemployment, male (% of male labor force) (national estimate)
1
unemployment, total (% of total labor force) (modeled ilo estimate)
1
unemployment, total (% of total labor force) (national estimate)
1
unemployment, youth female (% of female labor force ages 15-24) (modeled ilo estimate)
1
unemployment, youth female (% of female labor force ages 15-24) (national estimate)
1
unemployment, youth male (% of male labor force ages 15-24) (modeled ilo estimate)
1
unemployment, youth male (% of male labor force ages 15-24) (national estimate)
1
unemployment, youth total (% of total labor force ages 15-24) (modeled ilo estimate)
1
unemployment, youth total (% of total labor force ages 15-24) (national estimate)
1
vulnerable employment, female (% of female employment) (modeled ilo estimate)
1
vulnerable employment, male (% of male employment) (modeled ilo estimate)
1
vulnerable employment, total (% of total employment) (modeled ilo estimate)
1
wage and salaried workers, female (% of female employment) (modeled ilo estimate)
1
wage and salaried workers, male (% of male employment) (modeled ilo estimate)
1
wage and salaried workers, total (% of total employment) (modeled ilo estimate)
1
water productivity, total (constant 2015 us$ gdp per cubic meter of total freshwater withdrawal)
1 # t(nepal_df)
df_t <- transpose(nepal_df)
rownames(df_t) <- colnames(nepal_df)
colnames(df_t) <- rownames(nepal_df)
# Rename the columns with the first row. Columns are not properly renamed from above lines.
colnames(df_t) <- df_t[1,]
# Remove the first row.
df_t <- df_t[-1:-2,]
nepal_df <- df_t
nepal_dfnepal_df[0]df_t <- nepal_df %>% mutate_if(is.character, as.numeric)[1m[33mError[39m in `pull()`:[22m
[33m![39m Problem while evaluating `tibble_vars[[.env$i]]`.
[1mCaused by error:[22m
[1m[22m[33m![39m `data` must be uniquely named but has duplicate columns
[90mRun `rlang::last_error()` to see where the error occurred.[39m'length(x) = 10 > 1' in coercion to 'logical(1)''length(x) = 10 > 1' in coercion to 'logical(1)'cor(nepal_df)Error in cor(nepal_df) : 'x' must be numericplot(nepal_df)Add a new chunk by clicking the Insert Chunk button on the toolbar or by pressing Ctrl+Alt+I.
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